Physics-Informed Machine Learning for Geotechnical Engineering (Erasmus+ Blended Intensive Programme)

The aim of this program is to gain an understanding of the principles behind physics-informed machine learning, as well as to explore various scenarios in which these methods can be applied. The course consists of two parts: an online introductory course and a workshop that will be held on-site at RWTH.

1. Online introduction (13 –17 July)

During this week, the fundamentals of machine learning and its application in engineering are introduced. Topics include datapreparation, optimization techniques, and basic machine learning models. A significant focus is placed onphysics-informedneural networks(PINNs), their architecture, and their connection to gradients and differential equations. The week also includes an introduction to Python and scientific computing tools, with hands-on demonstrations using JupyterNotebooks.

2. Workshop at RWTH (20 –24 July)

During this week, the students will apply their newly gained theoretical knowledge in a hackathon-style workshop. Different tasks and problems will be provided, which will be solved by the students in a collaborative and informal setting. The goal of this session is to deepen the skills gained in the theoretical seminar and to share knowledge among participants. The practical nature of the workshop will also lead to a better understanding of the implementation, chances, and limitations of physics-informed machine learning methods. Last but not least, the hackathon is also a great opportunity to crack interesting modelling problems and network with students and faculties from different countries.

The two-level mutual trust-based quality assurance scheme has been adopted:

• at the university level: RWTH Aachen has applied its internal quality assurance procedures and structures to the proposal of Physics-Informed Machine Learning for Geotechnical Engineering (Erasmus+ Blended Intensive Program) it submitted to ENHANCE and to its implementation - the related learning activities,
• at the Alliance level: the body composed of Education Officers has made decisions regarding the inclusion of Physics-Informed Machine Learning for Geotechnical Engineering (Erasmus+ Blended Intensive Program) proposed by RWTH Aachen to the Innovative Learning Campus part of the joint ENHANCE educational offer, based on the compliance with the formal requirements and ENHANCE goals.

Final presentation

About recognition: Please contact the examination board at your home university to have the credits earned in this course recognized.

Application Deadline: 30th April 2026 --> Application Link (Please register with your First name, Surname and e-mail address)

The Summer School offers 20 places for students with Erasmus+ BIP status. Funding might be available.

There are 10 extra places for students without Erasmus+ BIP status. For that, please contact your local ENHANCE Mobility/Education Officer or check your university website for more information.

Agenda and room placement will be published in due time. During the presence-week evenings we will offer some social after-hours activities such as a City Tour and a Pub Crawl.